Abstract
With the rapid development of data science and modeling engineering, the capacity of cyberspace has significantly expanded which enables the online storage of increasing number of 3D models. Hence, the development of effective and efficient approaches to search 3D models is becoming increasingly important and urgent. In this paper, we propose a new sketch-based 3D retrieval framework named REBOR under the inspiration of retina which is not only consistent with human perception sensitivity but also simplifies the requirement of retrieval query by enabling hand-drawn sketch. The feature extraction process incorporates human visual system by simulating the ganglion perceptive mechanism in retina. Support Vector Machine is used to classify the query sketches and is further optimized by means of an global optimization algorithm so as to acquire optimal results automatically. Experiments are done on the database generated by ourselves with 15 categories of 3D objects, and the results indicate the effectiveness of REBOR in terms of retrieval accuracy.
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Alexandre A, Raphael O, Pierre V (2012) Freak: Fast retina keypoint. In: IEEE conference on computer vision and pattern recognition, https://doi.org/10.1109/CVPR.2012.6247715
Calonder M, Lepetit V, Strecha C et al (2010) Brief: binary robust independent elementary features. In: European conference on computer vision, https://doi.org/10.1007/978-3-642-15561-1_56
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018
Das GP, Vance PJ, Kerr D, Coleman SA, McGinnity TM, Liu JK (2019) Computational modelling of salamander retinal ganglion cells using machine learning approaches. Neurocomputing 325:101–112. https://doi.org/10.1016/j.neucom.2018.10.004
Deng J, Dong W, Socher R, Li L, Li K, Feifei L (2009) Imagenet: A large-scale hierarchical image database. Comput Vision Pattern Recognit 248–255
Duan L, Li W, Tsang IW, Xu D (2011) Improving web image search by bag-based reranking. IEEE Trans Image Process 20(11):3280–3290. https://doi.org/10.1109/TIP.2011.2159227
Gao R, Asano SM, Upadhyayula S et al (2019) Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution. Science 363:245–246. https://doi.org/10.1126/science.aau8302
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Comput Vision Pattern Recognit 770–778
Herbert B, Tinne T, Van GL (2006) Surf: Speeded up robust features. In: European Conference on Computer Vision, https://doi.org/10.1007/11744023_32
Itti Laurent, Koch C (2001) Computational modelling of visual attention. Nat Rev Neurosci 2:194–203. https://doi.org/10.1038/35058500
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39:459–471. https://doi.org/10.1007/s10898-007-9149-x
Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: State of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19. https://doi.org/10.1145/1126004.1126005
Li B, Johan H (2013) 3d model retrieval using hybrid features and class information. Multimed Tools Appl 62(3):821–846. https://doi.org/10.1007/s11042-011-0873-3
Li Y, Lei H, Lin S, Luo G (2018a) A new sketch-based 3d model retrieval method by using composite features. Multimed Tools Appl 77 (2):2921–2944. https://doi.org/10.1007/s11042-017-4446-y
Li B, Lu Y, Johan H, Fares R (2017) Sketch-based 3d model retrieval utilizing adaptive view clustering and semantic information. Multimed Tools Appl 76(24):26603–26631. https://doi.org/10.1007/s11042-016-4187-3
Li Y, Miao Z, Wang J, Zhang Y (2018b) Nonlinear embedding neural codes for visual instance retrieval. Neurocomputing 275:1275–1281. https://doi.org/10.1016/j.neucom.2017.09.072
Li Y, Wang S, Tian Q, et al. (2015) A survey of recent advances in visual feature detection. Neurocomputing 149:736–751. https://doi.org/10.1016/j.neucom.2014.08.003
Lowe DG (1999) Object recognition from local scale-invariant features. In: iccv IEEE Computer Society, https://doi.org/10.1109/ICCV.1999.790410
Mair E, Hager GD, Burschka D et al (2010) Adaptive and generic corner detection based on the accelerated segment test. Europ Conference on Comput Vision 1:183–196. https://doi.org/10.1007/978-3-642-155-9_14
Martin JH, Yahata BD, Hundley JM, Mayer JA, Schaedler TA, Pollock TM (2017) 3d printing of high-strength aluminium alloys. Nature 549 (7672):365–369. https://doi.org/10.1038/nature23894
Medathati NVK, Neumann H, Masson GS, Kornprobst P (2016) Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision. Comput Vision Image Understand 150:1–30
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. Comput Vision Pattern Recognit 4510–4520
Stefan L, Margarita C, Roland SY (2011) Brisk: Binary robust invariant scalable keypoints. In: International conference on computer vision, https://doi.org/10.1109/ICCV.2011.6126542
Tangelder JWH, Veltkamp RC (2008) A survey of content based 3d shape retrieval methods. Multimed Tools Appl 39(3):441–471. https://doi.org/10.1007/s11042-007-0181-0
Ungerleider Sabine KG (2000) Mechanisms of visual attention in the human cortex. Ann Rev Neurosci 23:315–41. https://doi.org/10.1146/annurev.neuro.23.1.315
Vanrullen R, Thorpe SJ (2002) Surfing a spike wave down the ventral stream. Vis Res 42(23):2593–2615
Wang D, Wang B, Zhao S, Yao H, Liu H (2017) View-based 3d object retrieval with discriminative views. Neurocomputing 252:58–66. https://doi.org/10.1016/j.neucom.2016.06.095
Weng D, Wang Y, Gong M, Tao D, Wei H, Huang D (2015) Derf: Distinctive efficient robust features from the biological modeling of the p ganglion cells. IEEE Trans Image Process 24(8):2287–2302
Wu L, Jin R, Jain AK (2013) Tag completion for image retrieval. IEEE Trans Pattern Anal Machine Intell 35(3):716–727. https://doi.org/10.1109/TPAMI.2012.124
Yang Y, Li B, Li P, Liu Q (2018) A two-stage clustering based 3d visual saliency model for dynamic scenarios. IEEE Trans Multimed 21 (4):809–820. https://doi.org/10.1109/tmm.2018.2867742
Yao Q, Cosme JGL, Xu T, Miszuk JM, Picciani PHS, Fong H, Sun H (2017) Three dimensional electrospun pcl/pla blend nanofibrous scaffolds with significantly improved stem cells osteogenic differentiation and cranial bone formation. Biomaterials 115:115–127. https://doi.org/10.1016/j.biomaterials.2016.11.018
Zhao X, Gregor R, Mavrids P, Schreck T (2017) Sketch-based 3d object retrieval with skeleton line views - initial results and research problems. In: EG workshop on 3D object retrieval
Zhao X, Mavrids P, Schreck T (2019) A high-performance approach for enhancingthe clarityof hand-drawn sketch images. Basic Sci J Textile Univ 31(2):237–260
Acknowledgements
This work is sponsored by the National Natural Science Foundation of China under Grant No.61806160 and Shaanxi Association for Science and Technology of Colleges and Universities Youth Talent Development Program, No. 20190112 and the Youth Innovation Team of Shaanxi Universities and Shaanxi Province Technical Innovation Foundation(2020CGXNG-012).
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Shi, X., Chen, H. & Zhao, X. REBOR: A new sketch-based 3d object retrieval framework using retina inspired features. Multimed Tools Appl 80, 23297–23311 (2021). https://doi.org/10.1007/s11042-021-10618-4
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DOI: https://doi.org/10.1007/s11042-021-10618-4